Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations2154048
Missing cells14380032
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory312.2 MiB
Average record size in memory152.0 B

Variable types

Text2
Categorical3
Numeric13
Boolean1

Alerts

visiting_client has constant value "False"Constant
churn is highly overall correlated with regularityHigh correlation
frequency_activating_top_pack is highly overall correlated with income_over_90days_3 and 6 other fieldsHigh correlation
income_over_90days_3 is highly overall correlated with frequency_activating_top_pack and 7 other fieldsHigh correlation
inter_espresso_call is highly overall correlated with income_over_90days_3 and 4 other fieldsHigh correlation
monthly_income is highly overall correlated with frequency_activating_top_pack and 7 other fieldsHigh correlation
num_refill_amount is highly overall correlated with frequency_activating_top_pack and 6 other fieldsHigh correlation
num_times_income_generated is highly overall correlated with frequency_activating_top_pack and 6 other fieldsHigh correlation
orange_calls is highly overall correlated with frequency_activating_top_pack and 6 other fieldsHigh correlation
regularity is highly overall correlated with churn and 7 other fieldsHigh correlation
topup_amount is highly overall correlated with frequency_activating_top_pack and 7 other fieldsHigh correlation
network_duration is highly imbalanced (86.4%)Imbalance
region has 849299 (39.4%) missing valuesMissing
topup_amount has 756739 (35.1%) missing valuesMissing
num_refill_amount has 756739 (35.1%) missing valuesMissing
monthly_income has 726048 (33.7%) missing valuesMissing
income_over_90days_3 has 726048 (33.7%) missing valuesMissing
num_times_income_generated has 726048 (33.7%) missing valuesMissing
num_of_connections has 1060433 (49.2%) missing valuesMissing
inter_espresso_call has 786675 (36.5%) missing valuesMissing
orange_calls has 895248 (41.6%) missing valuesMissing
tigo_calls has 1290016 (59.9%) missing valuesMissing
zone1_calls has 1984327 (92.1%) missing valuesMissing
zone2_calls has 2017224 (93.6%) missing valuesMissing
active_pack has 902594 (41.9%) missing valuesMissing
frequency_activating_top_pack has 902594 (41.9%) missing valuesMissing
num_of_connections is highly skewed (γ1 = 36.25674263)Skewed
zone1_calls is highly skewed (γ1 = 25.70889323)Skewed
zone2_calls is highly skewed (γ1 = 30.88518917)Skewed
user_id has unique valuesUnique
num_of_connections has 320153 (14.9%) zerosZeros
inter_espresso_call has 108046 (5.0%) zerosZeros
orange_calls has 61623 (2.9%) zerosZeros
tigo_calls has 94270 (4.4%) zerosZeros
zone1_calls has 59935 (2.8%) zerosZeros
zone2_calls has 40440 (1.9%) zerosZeros

Reproduction

Analysis started2024-10-14 08:19:46.781176
Analysis finished2024-10-14 08:20:53.521670
Duration1 minute and 6.74 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

user_id
Text

UNIQUE 

Distinct2154048
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:54.376488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters86161920
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2154048 ?
Unique (%)100.0%

Sample

1st row00000bfd7d50f01092811bc0c8d7b0d6fe7c3596
2nd row00000cb4a5d760de88fecb38e2f71b7bec52e834
3rd row00001654a9d9f96303d9969d0a4a851714a4bb57
4th row00001dd6fa45f7ba044bd5d84937be464ce78ac2
5th row000028d9e13a595abe061f9b58f3d76ab907850f
ValueCountFrequency (%)
00000bfd7d50f01092811bc0c8d7b0d6fe7c3596 1
 
< 0.1%
0000fe5ae46d696f27604fc70946b290277bff95 1
 
< 0.1%
00001dd6fa45f7ba044bd5d84937be464ce78ac2 1
 
< 0.1%
000028d9e13a595abe061f9b58f3d76ab907850f 1
 
< 0.1%
0000296564272665ccd2925d377e124f3306b01e 1
 
< 0.1%
00002b0ed56e2c199ec8c3021327229afa70f063 1
 
< 0.1%
0000313946b6849745963442c6e572d47cd24ced 1
 
< 0.1%
0000398021ccd3a488fa1a63dee3b2f0d471f9fd 1
 
< 0.1%
00003d165737109921ebd21f883cb8cff028b626 1
 
< 0.1%
0000527d276a6ba8b02810cc2c1d60d25e650f5f 1
 
< 0.1%
Other values (2154038) 2154038
> 99.9%
2024-10-14T11:20:55.192115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5393160
 
6.3%
b 5389543
 
6.3%
c 5387121
 
6.3%
0 5387072
 
6.3%
9 5385666
 
6.3%
d 5385205
 
6.3%
a 5384709
 
6.2%
5 5384389
 
6.2%
8 5384268
 
6.2%
4 5383987
 
6.2%
Other values (6) 32296800
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86161920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5393160
 
6.3%
b 5389543
 
6.3%
c 5387121
 
6.3%
0 5387072
 
6.3%
9 5385666
 
6.3%
d 5385205
 
6.3%
a 5384709
 
6.2%
5 5384389
 
6.2%
8 5384268
 
6.2%
4 5383987
 
6.2%
Other values (6) 32296800
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86161920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5393160
 
6.3%
b 5389543
 
6.3%
c 5387121
 
6.3%
0 5387072
 
6.3%
9 5385666
 
6.3%
d 5385205
 
6.3%
a 5384709
 
6.2%
5 5384389
 
6.2%
8 5384268
 
6.2%
4 5383987
 
6.2%
Other values (6) 32296800
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86161920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5393160
 
6.3%
b 5389543
 
6.3%
c 5387121
 
6.3%
0 5387072
 
6.3%
9 5385666
 
6.3%
d 5385205
 
6.3%
a 5384709
 
6.2%
5 5384389
 
6.2%
8 5384268
 
6.2%
4 5383987
 
6.2%
Other values (6) 32296800
37.5%

region
Categorical

MISSING 

Distinct14
Distinct (%)< 0.1%
Missing849299
Missing (%)39.4%
Memory size16.4 MiB
DAKAR
513271 
THIES
180052 
SAINT-LOUIS
119886 
LOUGA
99053 
KAOLACK
96986 
Other values (9)
295501 

Length

Max length11
Median length5
Mean length6.3267073
Min length5

Characters and Unicode

Total characters8254765
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFATICK
2nd rowDAKAR
3rd rowDAKAR
4th rowLOUGA
5th rowLOUGA

Common Values

ValueCountFrequency (%)
DAKAR 513271
23.8%
THIES 180052
 
8.4%
SAINT-LOUIS 119886
 
5.6%
LOUGA 99053
 
4.6%
KAOLACK 96986
 
4.5%
DIOURBEL 66911
 
3.1%
TAMBACOUNDA 55074
 
2.6%
KAFFRINE 43963
 
2.0%
KOLDA 38743
 
1.8%
FATICK 35643
 
1.7%
Other values (4) 55167
 
2.6%
(Missing) 849299
39.4%

Length

2024-10-14T11:20:55.264963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dakar 513271
39.3%
thies 180052
 
13.8%
saint-louis 119886
 
9.2%
louga 99053
 
7.6%
kaolack 96986
 
7.4%
diourbel 66911
 
5.1%
tambacounda 55074
 
4.2%
kaffrine 43963
 
3.4%
kolda 38743
 
3.0%
fatick 35643
 
2.7%
Other values (4) 55167
 
4.2%

Most occurring characters

ValueCountFrequency (%)
A 1781190
21.6%
K 826612
10.0%
D 678138
 
8.2%
R 646090
 
7.8%
I 613350
 
7.4%
O 503757
 
6.1%
S 422943
 
5.1%
L 421579
 
5.1%
T 419738
 
5.1%
U 368028
 
4.5%
Other values (10) 1573340
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8254765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1781190
21.6%
K 826612
10.0%
D 678138
 
8.2%
R 646090
 
7.8%
I 613350
 
7.4%
O 503757
 
6.1%
S 422943
 
5.1%
L 421579
 
5.1%
T 419738
 
5.1%
U 368028
 
4.5%
Other values (10) 1573340
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8254765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1781190
21.6%
K 826612
10.0%
D 678138
 
8.2%
R 646090
 
7.8%
I 613350
 
7.4%
O 503757
 
6.1%
S 422943
 
5.1%
L 421579
 
5.1%
T 419738
 
5.1%
U 368028
 
4.5%
Other values (10) 1573340
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8254765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1781190
21.6%
K 826612
10.0%
D 678138
 
8.2%
R 646090
 
7.8%
I 613350
 
7.4%
O 503757
 
6.1%
S 422943
 
5.1%
L 421579
 
5.1%
T 419738
 
5.1%
U 368028
 
4.5%
Other values (10) 1573340
19.1%

network_duration
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.4 MiB
K > 24 month
2043201 
I 18-21 month
 
45278
H 15-18 month
 
26006
G 12-15 month
 
14901
J 21-24 month
 
12725
Other values (3)
 
11937

Length

Max length13
Median length12
Mean length12.044707
Min length11

Characters and Unicode

Total characters25944877
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK > 24 month
2nd rowI 18-21 month
3rd rowK > 24 month
4th rowK > 24 month
5th rowK > 24 month

Common Values

ValueCountFrequency (%)
K > 24 month 2043201
94.9%
I 18-21 month 45278
 
2.1%
H 15-18 month 26006
 
1.2%
G 12-15 month 14901
 
0.7%
J 21-24 month 12725
 
0.6%
F 9-12 month 9328
 
0.4%
E 6-9 month 1839
 
0.1%
D 3-6 month 770
 
< 0.1%

Length

2024-10-14T11:20:55.309064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T11:20:55.364230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
month 2154048
25.3%
k 2043201
24.0%
2043201
24.0%
24 2043201
24.0%
i 45278
 
0.5%
18-21 45278
 
0.5%
h 26006
 
0.3%
15-18 26006
 
0.3%
12-15 14901
 
0.2%
g 14901
 
0.2%
Other values (8) 49324
 
0.6%

Most occurring characters

ValueCountFrequency (%)
6351297
24.5%
m 2154048
 
8.3%
o 2154048
 
8.3%
n 2154048
 
8.3%
t 2154048
 
8.3%
h 2154048
 
8.3%
2 2138158
 
8.2%
4 2055926
 
7.9%
K 2043201
 
7.9%
> 2043201
 
7.9%
Other values (14) 542854
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25944877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6351297
24.5%
m 2154048
 
8.3%
o 2154048
 
8.3%
n 2154048
 
8.3%
t 2154048
 
8.3%
h 2154048
 
8.3%
2 2138158
 
8.2%
4 2055926
 
7.9%
K 2043201
 
7.9%
> 2043201
 
7.9%
Other values (14) 542854
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25944877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6351297
24.5%
m 2154048
 
8.3%
o 2154048
 
8.3%
n 2154048
 
8.3%
t 2154048
 
8.3%
h 2154048
 
8.3%
2 2138158
 
8.2%
4 2055926
 
7.9%
K 2043201
 
7.9%
> 2043201
 
7.9%
Other values (14) 542854
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25944877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6351297
24.5%
m 2154048
 
8.3%
o 2154048
 
8.3%
n 2154048
 
8.3%
t 2154048
 
8.3%
h 2154048
 
8.3%
2 2138158
 
8.2%
4 2055926
 
7.9%
K 2043201
 
7.9%
> 2043201
 
7.9%
Other values (14) 542854
 
2.1%

topup_amount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6540
Distinct (%)0.5%
Missing756739
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean5532.117
Minimum10
Maximum470000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.414713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile250
Q11000
median3000
Q37350
95-th percentile18500
Maximum470000
Range469990
Interquartile range (IQR)6350

Descriptive statistics

Standard deviation7111.3394
Coefficient of variation (CV)1.2854644
Kurtosis57.528484
Mean5532.117
Median Absolute Deviation (MAD)2400
Skewness4.2297262
Sum7.7300769 × 109
Variance50571148
MonotonicityNot monotonic
2024-10-14T11:20:55.460690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 112976
 
5.2%
1000 82997
 
3.9%
1500 48710
 
2.3%
2000 46122
 
2.1%
200 40004
 
1.9%
3000 34831
 
1.6%
2500 32026
 
1.5%
4000 24109
 
1.1%
3500 23793
 
1.1%
100 20188
 
0.9%
Other values (6530) 931553
43.2%
(Missing) 756739
35.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
20 2
 
< 0.1%
22 1
 
< 0.1%
25 2
 
< 0.1%
30 2
 
< 0.1%
35 1
 
< 0.1%
37 1
 
< 0.1%
40 1
 
< 0.1%
48 1
 
< 0.1%
50 360
< 0.1%
ValueCountFrequency (%)
470000 1
< 0.1%
290500 1
< 0.1%
286500 1
< 0.1%
265000 1
< 0.1%
259500 1
< 0.1%
256000 1
< 0.1%
235500 1
< 0.1%
235000 1
< 0.1%
231000 2
< 0.1%
230600 1
< 0.1%

num_refill_amount
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct123
Distinct (%)< 0.1%
Missing756739
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean11.52912
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.506304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q316
95-th percentile40
Maximum133
Range132
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.27407
Coefficient of variation (CV)1.1513515
Kurtosis5.3169562
Mean11.52912
Median Absolute Deviation (MAD)5
Skewness2.1119879
Sum16109743
Variance176.20092
MonotonicityNot monotonic
2024-10-14T11:20:55.554705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 219471
 
10.2%
2 139897
 
6.5%
3 110506
 
5.1%
4 88889
 
4.1%
5 74527
 
3.5%
6 64115
 
3.0%
7 55616
 
2.6%
8 49983
 
2.3%
9 44715
 
2.1%
10 40655
 
1.9%
Other values (113) 508935
23.6%
(Missing) 756739
35.1%
ValueCountFrequency (%)
1 219471
10.2%
2 139897
6.5%
3 110506
5.1%
4 88889
4.1%
5 74527
 
3.5%
6 64115
 
3.0%
7 55616
 
2.6%
8 49983
 
2.3%
9 44715
 
2.1%
10 40655
 
1.9%
ValueCountFrequency (%)
133 1
 
< 0.1%
132 1
 
< 0.1%
131 1
 
< 0.1%
122 1
 
< 0.1%
121 1
 
< 0.1%
119 1
 
< 0.1%
118 1
 
< 0.1%
117 2
< 0.1%
115 4
< 0.1%
114 2
< 0.1%

monthly_income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38114
Distinct (%)2.7%
Missing726048
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean5510.8103
Minimum1
Maximum532177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.601935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile199
Q11000
median3000
Q37368
95-th percentile18791
Maximum532177
Range532176
Interquartile range (IQR)6368

Descriptive statistics

Standard deviation7187.1129
Coefficient of variation (CV)1.3041844
Kurtosis64.821825
Mean5510.8103
Median Absolute Deviation (MAD)2498
Skewness4.1890021
Sum7.8694372 × 109
Variance51654592
MonotonicityNot monotonic
2024-10-14T11:20:55.650731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 58783
 
2.7%
1000 36269
 
1.7%
1500 20740
 
1.0%
200 20043
 
0.9%
2000 18220
 
0.8%
3000 13211
 
0.6%
2500 12096
 
0.6%
3500 8727
 
0.4%
4000 8303
 
0.4%
100 7893
 
0.4%
Other values (38104) 1223715
56.8%
(Missing) 726048
33.7%
ValueCountFrequency (%)
1 4295
0.2%
2 3134
0.1%
3 211
 
< 0.1%
4 1961
0.1%
5 104
 
< 0.1%
6 1111
 
0.1%
7 522
 
< 0.1%
8 1225
 
0.1%
9 1230
 
0.1%
10 2691
0.1%
ValueCountFrequency (%)
532177 1
< 0.1%
397968 1
< 0.1%
323541 1
< 0.1%
272191 1
< 0.1%
266050 1
< 0.1%
244001 1
< 0.1%
240094 1
< 0.1%
233583 1
< 0.1%
233413 1
< 0.1%
233141 1
< 0.1%

income_over_90days_3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16535
Distinct (%)1.2%
Missing726048
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean1836.9429
Minimum0
Maximum177392
Zeros4295
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.696726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q1333
median1000
Q32456
95-th percentile6264
Maximum177392
Range177392
Interquartile range (IQR)2123

Descriptive statistics

Standard deviation2395.7
Coefficient of variation (CV)1.3041777
Kurtosis64.822078
Mean1836.9429
Median Absolute Deviation (MAD)833
Skewness4.1890192
Sum2.6231545 × 109
Variance5739378.3
MonotonicityNot monotonic
2024-10-14T11:20:55.745433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167 67878
 
3.2%
333 43705
 
2.0%
500 28568
 
1.3%
667 22898
 
1.1%
67 22753
 
1.1%
1000 18483
 
0.9%
833 15341
 
0.7%
1167 11524
 
0.5%
1333 10725
 
0.5%
33 10473
 
0.5%
Other values (16525) 1175652
54.6%
(Missing) 726048
33.7%
ValueCountFrequency (%)
0 4295
0.2%
1 5306
0.2%
2 1737
 
0.1%
3 5146
0.2%
4 2755
0.1%
5 1819
 
0.1%
6 1175
 
0.1%
7 3439
0.2%
8 728
 
< 0.1%
9 1090
 
0.1%
ValueCountFrequency (%)
177392 1
< 0.1%
132656 1
< 0.1%
107847 1
< 0.1%
90730 1
< 0.1%
88683 1
< 0.1%
81334 1
< 0.1%
80031 1
< 0.1%
77861 1
< 0.1%
77804 1
< 0.1%
77714 1
< 0.1%

num_times_income_generated
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct91
Distinct (%)< 0.1%
Missing726048
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean13.978141
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.794806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q320
95-th percentile45
Maximum91
Range90
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.694035
Coefficient of variation (CV)1.0512152
Kurtosis3.402515
Mean13.978141
Median Absolute Deviation (MAD)7
Skewness1.7750807
Sum19960786
Variance215.91466
MonotonicityNot monotonic
2024-10-14T11:20:55.840083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 161585
 
7.5%
2 116460
 
5.4%
3 95237
 
4.4%
4 82338
 
3.8%
5 71867
 
3.3%
6 64228
 
3.0%
7 57343
 
2.7%
8 51893
 
2.4%
9 47532
 
2.2%
10 43694
 
2.0%
Other values (81) 635823
29.5%
(Missing) 726048
33.7%
ValueCountFrequency (%)
1 161585
7.5%
2 116460
5.4%
3 95237
4.4%
4 82338
3.8%
5 71867
3.3%
6 64228
 
3.0%
7 57343
 
2.7%
8 51893
 
2.4%
9 47532
 
2.2%
10 43694
 
2.0%
ValueCountFrequency (%)
91 83
 
< 0.1%
90 84
 
< 0.1%
89 126
 
< 0.1%
88 165
 
< 0.1%
87 214
< 0.1%
86 291
< 0.1%
85 268
< 0.1%
84 322
< 0.1%
83 362
< 0.1%
82 481
< 0.1%

num_of_connections
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct41550
Distinct (%)3.8%
Missing1060433
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean3366.4502
Minimum0
Maximum1823866
Zeros320153
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.884747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median257
Q32895
95-th percentile14981
Maximum1823866
Range1823866
Interquartile range (IQR)2895

Descriptive statistics

Standard deviation13304.464
Coefficient of variation (CV)3.952075
Kurtosis2448.1241
Mean3366.4502
Median Absolute Deviation (MAD)257
Skewness36.256743
Sum3.6816004 × 109
Variance1.7700875 × 108
MonotonicityNot monotonic
2024-10-14T11:20:55.934043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 320153
 
14.9%
1 41366
 
1.9%
2 13233
 
0.6%
3 7326
 
0.3%
4 5613
 
0.3%
1024 5469
 
0.3%
5 4678
 
0.2%
1023 3794
 
0.2%
6 3778
 
0.2%
7 3202
 
0.1%
Other values (41540) 685003
31.8%
(Missing) 1060433
49.2%
ValueCountFrequency (%)
0 320153
14.9%
1 41366
 
1.9%
2 13233
 
0.6%
3 7326
 
0.3%
4 5613
 
0.3%
5 4678
 
0.2%
6 3778
 
0.2%
7 3202
 
0.1%
8 2920
 
0.1%
9 2837
 
0.1%
ValueCountFrequency (%)
1823866 1
< 0.1%
1702309 1
< 0.1%
1556829 1
< 0.1%
1352304 1
< 0.1%
1326875 1
< 0.1%
1297464 1
< 0.1%
1272720 1
< 0.1%
1238915 1
< 0.1%
1154809 1
< 0.1%
1117735 1
< 0.1%

inter_espresso_call
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9884
Distinct (%)0.7%
Missing786675
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean277.68914
Minimum0
Maximum50809
Zeros108046
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:55.983430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median27
Q3156
95-th percentile1358
Maximum50809
Range50809
Interquartile range (IQR)151

Descriptive statistics

Standard deviation872.68891
Coefficient of variation (CV)3.1426829
Kurtosis116.85712
Mean277.68914
Median Absolute Deviation (MAD)26
Skewness8.1479278
Sum3.7970463 × 108
Variance761585.93
MonotonicityNot monotonic
2024-10-14T11:20:56.031743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 108046
 
5.0%
1 92118
 
4.3%
2 58773
 
2.7%
3 42296
 
2.0%
7 41382
 
1.9%
4 38699
 
1.8%
8 38501
 
1.8%
5 29845
 
1.4%
6 29496
 
1.4%
9 19640
 
0.9%
Other values (9874) 868577
40.3%
(Missing) 786675
36.5%
ValueCountFrequency (%)
0 108046
5.0%
1 92118
4.3%
2 58773
2.7%
3 42296
 
2.0%
4 38699
 
1.8%
5 29845
 
1.4%
6 29496
 
1.4%
7 41382
 
1.9%
8 38501
 
1.8%
9 19640
 
0.9%
ValueCountFrequency (%)
50809 1
< 0.1%
45011 1
< 0.1%
38648 1
< 0.1%
36687 1
< 0.1%
34105 1
< 0.1%
33452 1
< 0.1%
32141 1
< 0.1%
31768 1
< 0.1%
30425 1
< 0.1%
29861 1
< 0.1%

orange_calls
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct3167
Distinct (%)0.3%
Missing895248
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean95.418711
Minimum0
Maximum21323
Zeros61623
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:56.079050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median29
Q399
95-th percentile392
Maximum21323
Range21323
Interquartile range (IQR)92

Descriptive statistics

Standard deviation204.98727
Coefficient of variation (CV)2.1482921
Kurtosis189.03871
Mean95.418711
Median Absolute Deviation (MAD)27
Skewness8.0540159
Sum1.2011307 × 108
Variance42019.779
MonotonicityNot monotonic
2024-10-14T11:20:56.125706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 68881
 
3.2%
0 61623
 
2.9%
2 49435
 
2.3%
3 36198
 
1.7%
4 33933
 
1.6%
8 25826
 
1.2%
5 24649
 
1.1%
6 22373
 
1.0%
7 21218
 
1.0%
10 20250
 
0.9%
Other values (3157) 894414
41.5%
(Missing) 895248
41.6%
ValueCountFrequency (%)
0 61623
2.9%
1 68881
3.2%
2 49435
2.3%
3 36198
1.7%
4 33933
1.6%
5 24649
 
1.1%
6 22373
 
1.0%
7 21218
 
1.0%
8 25826
 
1.2%
9 19954
 
0.9%
ValueCountFrequency (%)
21323 1
< 0.1%
12040 1
< 0.1%
7660 1
< 0.1%
7314 1
< 0.1%
6788 1
< 0.1%
6721 1
< 0.1%
6555 1
< 0.1%
6429 1
< 0.1%
6416 1
< 0.1%
6319 1
< 0.1%

tigo_calls
Real number (ℝ)

MISSING  ZEROS 

Distinct1315
Distinct (%)0.2%
Missing1290016
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean23.109253
Minimum0
Maximum4174
Zeros94270
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:56.172790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q320
95-th percentile95
Maximum4174
Range4174
Interquartile range (IQR)18

Descriptive statistics

Standard deviation63.578086
Coefficient of variation (CV)2.7511961
Kurtosis334.67472
Mean23.109253
Median Absolute Deviation (MAD)5
Skewness12.899932
Sum19967134
Variance4042.173
MonotonicityNot monotonic
2024-10-14T11:20:56.217791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 112165
 
5.2%
0 94270
 
4.4%
2 72530
 
3.4%
3 53003
 
2.5%
4 42980
 
2.0%
5 34524
 
1.6%
6 29421
 
1.4%
7 26170
 
1.2%
8 24304
 
1.1%
9 20835
 
1.0%
Other values (1305) 353830
 
16.4%
(Missing) 1290016
59.9%
ValueCountFrequency (%)
0 94270
4.4%
1 112165
5.2%
2 72530
3.4%
3 53003
2.5%
4 42980
 
2.0%
5 34524
 
1.6%
6 29421
 
1.4%
7 26170
 
1.2%
8 24304
 
1.1%
9 20835
 
1.0%
ValueCountFrequency (%)
4174 1
< 0.1%
3800 1
< 0.1%
3728 1
< 0.1%
3706 1
< 0.1%
3658 1
< 0.1%
3486 1
< 0.1%
2955 1
< 0.1%
2899 1
< 0.1%
2860 1
< 0.1%
2796 1
< 0.1%

zone1_calls
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct612
Distinct (%)0.4%
Missing1984327
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean8.1701322
Minimum0
Maximum4792
Zeros59935
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:56.262929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile32
Maximum4792
Range4792
Interquartile range (IQR)3

Descriptive statistics

Standard deviation41.169511
Coefficient of variation (CV)5.0390264
Kurtosis1572.6889
Mean8.1701322
Median Absolute Deviation (MAD)1
Skewness25.708893
Sum1386643
Variance1694.9287
MonotonicityNot monotonic
2024-10-14T11:20:56.312594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59935
 
2.8%
1 41376
 
1.9%
2 16858
 
0.8%
3 9264
 
0.4%
4 6044
 
0.3%
5 4434
 
0.2%
6 3233
 
0.2%
7 2573
 
0.1%
8 2134
 
0.1%
9 2060
 
0.1%
Other values (602) 21810
 
1.0%
(Missing) 1984327
92.1%
ValueCountFrequency (%)
0 59935
2.8%
1 41376
1.9%
2 16858
 
0.8%
3 9264
 
0.4%
4 6044
 
0.3%
5 4434
 
0.2%
6 3233
 
0.2%
7 2573
 
0.1%
8 2134
 
0.1%
9 2060
 
0.1%
ValueCountFrequency (%)
4792 1
< 0.1%
2507 1
< 0.1%
1986 1
< 0.1%
1867 1
< 0.1%
1839 1
< 0.1%
1804 1
< 0.1%
1730 1
< 0.1%
1684 1
< 0.1%
1659 1
< 0.1%
1657 1
< 0.1%

zone2_calls
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct486
Distinct (%)0.4%
Missing2017224
Missing (%)93.6%
Infinite0
Infinite (%)0.0%
Mean7.5533094
Minimum0
Maximum3697
Zeros40440
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:56.356862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile29
Maximum3697
Range3697
Interquartile range (IQR)5

Descriptive statistics

Standard deviation33.487234
Coefficient of variation (CV)4.4334519
Kurtosis2107.0549
Mean7.5533094
Median Absolute Deviation (MAD)2
Skewness30.885189
Sum1033474
Variance1121.3948
MonotonicityNot monotonic
2024-10-14T11:20:56.405543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40440
 
1.9%
1 26941
 
1.3%
2 15428
 
0.7%
3 9857
 
0.5%
4 7393
 
0.3%
5 4836
 
0.2%
6 3723
 
0.2%
7 3231
 
0.1%
8 2360
 
0.1%
9 2074
 
0.1%
Other values (476) 20541
 
1.0%
(Missing) 2017224
93.6%
ValueCountFrequency (%)
0 40440
1.9%
1 26941
1.3%
2 15428
 
0.7%
3 9857
 
0.5%
4 7393
 
0.3%
5 4836
 
0.2%
6 3723
 
0.2%
7 3231
 
0.1%
8 2360
 
0.1%
9 2074
 
0.1%
ValueCountFrequency (%)
3697 1
< 0.1%
3143 1
< 0.1%
2008 1
< 0.1%
1796 1
< 0.1%
1618 1
< 0.1%
1351 1
< 0.1%
1346 1
< 0.1%
1324 1
< 0.1%
1321 1
< 0.1%
1316 1
< 0.1%

visiting_client
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
False
2154048 
ValueCountFrequency (%)
False 2154048
100.0%
2024-10-14T11:20:56.447647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

regularity
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.042505
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:56.485937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median24
Q351
95-th percentile62
Maximum62
Range61
Interquartile range (IQR)45

Descriptive statistics

Standard deviation22.286857
Coefficient of variation (CV)0.79475271
Kurtosis-1.4871698
Mean28.042505
Median Absolute Deviation (MAD)20
Skewness0.24740754
Sum60404902
Variance496.70399
MonotonicityNot monotonic
2024-10-14T11:20:56.533358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 195162
 
9.1%
62 166477
 
7.7%
2 118915
 
5.5%
3 86027
 
4.0%
4 68335
 
3.2%
61 64431
 
3.0%
5 56823
 
2.6%
6 49771
 
2.3%
60 47515
 
2.2%
7 44483
 
2.1%
Other values (52) 1256109
58.3%
ValueCountFrequency (%)
1 195162
9.1%
2 118915
5.5%
3 86027
4.0%
4 68335
 
3.2%
5 56823
 
2.6%
6 49771
 
2.3%
7 44483
 
2.1%
8 41208
 
1.9%
9 37397
 
1.7%
10 34883
 
1.6%
ValueCountFrequency (%)
62 166477
7.7%
61 64431
 
3.0%
60 47515
 
2.2%
59 39821
 
1.8%
58 34710
 
1.6%
57 31831
 
1.5%
56 29166
 
1.4%
55 27491
 
1.3%
54 26417
 
1.2%
53 25147
 
1.2%

active_pack
Text

MISSING 

Distinct140
Distinct (%)< 0.1%
Missing902594
Missing (%)41.9%
Memory size16.4 MiB
2024-10-14T11:20:56.614499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length42
Mean length23.185356
Min length7

Characters and Unicode

Total characters29015406
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st rowOn net 200F=Unlimited _call24H
2nd rowOn-net 1000F=10MilF;10d
3rd rowData:1000F=5GB,7d
4th rowMixt 250F=Unlimited_call24H
5th rowMIXT:500F= 2500F on net _2500F off net;2d
ValueCountFrequency (%)
all-net 387647
 
12.4%
500f=2000f;5d 317802
 
10.2%
net 257671
 
8.3%
on 238197
 
7.6%
200f=unlimited 152295
 
4.9%
call24h 152295
 
4.9%
2500f 128824
 
4.1%
data 127980
 
4.1%
data:490f=1gb,7d 115180
 
3.7%
mixt 91930
 
2.9%
Other values (186) 1152864
36.9%
2024-10-14T11:20:56.759967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4097436
 
14.1%
1871231
 
6.4%
l 1758228
 
6.1%
F 1688162
 
5.8%
t 1615813
 
5.6%
n 1458987
 
5.0%
2 1358477
 
4.7%
e 1172573
 
4.0%
a 1113680
 
3.8%
5 1106572
 
3.8%
Other values (61) 11774247
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29015406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4097436
 
14.1%
1871231
 
6.4%
l 1758228
 
6.1%
F 1688162
 
5.8%
t 1615813
 
5.6%
n 1458987
 
5.0%
2 1358477
 
4.7%
e 1172573
 
4.0%
a 1113680
 
3.8%
5 1106572
 
3.8%
Other values (61) 11774247
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29015406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4097436
 
14.1%
1871231
 
6.4%
l 1758228
 
6.1%
F 1688162
 
5.8%
t 1615813
 
5.6%
n 1458987
 
5.0%
2 1358477
 
4.7%
e 1172573
 
4.0%
a 1113680
 
3.8%
5 1106572
 
3.8%
Other values (61) 11774247
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29015406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4097436
 
14.1%
1871231
 
6.4%
l 1758228
 
6.1%
F 1688162
 
5.8%
t 1615813
 
5.6%
n 1458987
 
5.0%
2 1358477
 
4.7%
e 1172573
 
4.0%
a 1113680
 
3.8%
5 1106572
 
3.8%
Other values (61) 11774247
40.6%

frequency_activating_top_pack
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct245
Distinct (%)< 0.1%
Missing902594
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean9.2724615
Minimum1
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2024-10-14T11:20:56.821089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile33
Maximum713
Range712
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.280443
Coefficient of variation (CV)1.3243995
Kurtosis61.726468
Mean9.2724615
Median Absolute Deviation (MAD)4
Skewness4.1120661
Sum11604059
Variance150.80928
MonotonicityNot monotonic
2024-10-14T11:20:56.870200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 251882
 
11.7%
2 155396
 
7.2%
3 116447
 
5.4%
4 85552
 
4.0%
5 68531
 
3.2%
6 57092
 
2.7%
7 49478
 
2.3%
8 43188
 
2.0%
9 38731
 
1.8%
10 34641
 
1.6%
Other values (235) 350516
 
16.3%
(Missing) 902594
41.9%
ValueCountFrequency (%)
1 251882
11.7%
2 155396
7.2%
3 116447
5.4%
4 85552
 
4.0%
5 68531
 
3.2%
6 57092
 
2.7%
7 49478
 
2.3%
8 43188
 
2.0%
9 38731
 
1.8%
10 34641
 
1.6%
ValueCountFrequency (%)
713 1
< 0.1%
629 1
< 0.1%
624 1
< 0.1%
612 1
< 0.1%
592 1
< 0.1%
560 1
< 0.1%
544 1
< 0.1%
511 1
< 0.1%
452 1
< 0.1%
433 1
< 0.1%

churn
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.4 MiB
0
1750062 
1
403986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2154048
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1750062
81.2%
1 403986
 
18.8%

Length

2024-10-14T11:20:56.914045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T11:20:56.948291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1750062
81.2%
1 403986
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 1750062
81.2%
1 403986
 
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2154048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1750062
81.2%
1 403986
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2154048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1750062
81.2%
1 403986
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2154048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1750062
81.2%
1 403986
 
18.8%

Interactions

2024-10-14T11:20:41.418349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:27.560432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.832336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.166792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.363395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.614810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.132734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.148075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.265748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.452791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.339310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.081811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.999528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.516686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:27.678987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.945732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.270322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.471982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.722395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.220408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.240636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.366457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.528102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.388661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.131020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.111330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.615780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:27.817743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.055491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.377184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.589394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.834610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.297464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.338631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.474163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.609145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.439033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.179872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.256028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.715833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:27.924904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.166583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.482250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.696835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.941870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.371481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.433971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.575501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.687021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.488669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.230375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.372036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.810429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.033265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.280887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.587732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.799939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.059886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.461269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.526754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.678549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.767519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.587753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.281014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.512215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.881842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.111838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.364017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.669054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.881463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.143079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.547732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.599589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.765928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.830932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.693543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.325461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.641065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.976941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.214855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.476024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.770579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.981912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.248279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.626591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.696386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.858875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.905370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.745819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.371471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.787523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:42.069667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.310983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.585750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.865867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.078963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.654981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.698822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.784009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.950458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.984929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.799481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.421982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.889239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:42.146631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.390649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.674399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.944602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.170488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.736091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.764759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.856859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.023021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.050408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.846135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.466769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:40.970161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:42.194355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.444264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.734407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.995172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.224342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.785314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.809879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.909543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.075020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.095407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.890089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.509972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.022249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:42.242047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.496835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.789155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.043757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.275293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.833647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.854587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.957323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.126623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.141906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.930448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.554276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.071433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:42.340520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.608108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:29.932415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.152512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.399228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:33.953490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.957750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.071598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.247177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.215611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.981593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.603198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.214843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:42.436081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:28.712812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:30.056550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:31.258693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:32.503844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:34.054277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:35.036634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:36.164250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:37.368027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:38.287523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.032084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:39.651474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-14T11:20:41.319402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-14T11:20:56.980432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
churnfrequency_activating_top_packincome_over_90days_3inter_espresso_callmonthly_incomenetwork_durationnum_of_connectionsnum_refill_amountnum_times_income_generatedorange_callsregionregularitytigo_callstopup_amountzone1_callszone2_calls
churn1.0000.0110.0080.0170.0080.0500.0010.1080.1480.0070.0340.5570.0070.0090.0050.009
frequency_activating_top_pack0.0111.0000.8170.4360.8170.0000.2290.8940.8670.5360.0100.5970.3500.8120.0980.065
income_over_90days_30.0080.8171.0000.5191.0000.0050.3890.8790.8800.6790.0090.7160.4530.9870.2190.311
inter_espresso_call0.0170.4360.5191.0000.5190.000-0.0980.4760.4380.5510.0080.5230.3680.5090.065-0.023
monthly_income0.0080.8171.0000.5191.0000.0050.3890.8790.8800.6780.0090.7160.4530.9870.2190.311
network_duration0.0500.0000.0050.0000.0051.0000.0170.0040.0050.0150.0230.0160.0000.0060.0040.009
num_of_connections0.0010.2290.389-0.0980.3890.0171.0000.2960.331-0.0210.0040.302-0.0140.379-0.022-0.000
num_refill_amount0.1080.8940.8790.4760.8790.0040.2961.0000.9510.5620.0460.6780.3620.8870.0880.185
num_times_income_generated0.1480.8670.8800.4380.8800.0050.3310.9511.0000.5280.0530.6910.3350.8710.0830.194
orange_calls0.0070.5360.6790.5510.6780.015-0.0210.5620.5281.0000.0080.4570.4710.6690.1250.049
region0.0340.0100.0090.0080.0090.0230.0040.0460.0530.0081.0000.0360.0070.0110.0050.000
regularity0.5570.5970.7160.5230.7160.0160.3020.6780.6910.4570.0361.0000.3230.7070.0540.043
tigo_calls0.0070.3500.4530.3680.4530.000-0.0140.3620.3350.4710.0070.3231.0000.4490.0770.021
topup_amount0.0090.8120.9870.5090.9870.0060.3790.8870.8710.6690.0110.7070.4491.0000.2150.309
zone1_calls0.0050.0980.2190.0650.2190.004-0.0220.0880.0830.1250.0050.0540.0770.2151.0000.107
zone2_calls0.0090.0650.311-0.0230.3110.009-0.0000.1850.1940.0490.0000.0430.0210.3090.1071.000

Missing values

2024-10-14T11:20:42.786634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-14T11:20:44.375266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-14T11:20:52.089458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

user_idregionnetwork_durationtopup_amountnum_refill_amountmonthly_incomeincome_over_90days_3num_times_income_generatednum_of_connectionsinter_espresso_callorange_callstigo_callszone1_callszone2_callsvisiting_clientregularityactive_packfrequency_activating_top_packchurn
000000bfd7d50f01092811bc0c8d7b0d6fe7c3596FATICKK > 24 month4250.015.04251.01417.017.04.0388.046.01.01.02.0NO54On net 200F=Unlimited _call24H8.00
100000cb4a5d760de88fecb38e2f71b7bec52e834NaNI 18-21 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO4NaNNaN1
200001654a9d9f96303d9969d0a4a851714a4bb57NaNK > 24 month3600.02.01020.0340.02.0NaN90.046.07.0NaNNaNNO17On-net 1000F=10MilF;10d1.00
300001dd6fa45f7ba044bd5d84937be464ce78ac2DAKARK > 24 month13500.015.013502.04501.018.043804.041.0102.02.0NaNNaNNO62Data:1000F=5GB,7d11.00
4000028d9e13a595abe061f9b58f3d76ab907850fDAKARK > 24 month1000.01.0985.0328.01.0NaN39.024.0NaNNaNNaNNO11Mixt 250F=Unlimited_call24H2.00
50000296564272665ccd2925d377e124f3306b01eLOUGAK > 24 month8500.017.09000.03000.018.0NaN252.070.091.0NaNNaNNO62MIXT:500F= 2500F on net _2500F off net;2d18.00
600002b0ed56e2c199ec8c3021327229afa70f063LOUGAK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO2NaNNaN0
70000313946b6849745963442c6e572d47cd24cedDAKARK > 24 month7000.016.07229.02410.022.01601.077.029.0100.0NaNNaNNO55All-net 500F=2000F;5d8.00
80000398021ccd3a488fa1a63dee3b2f0d471f9fdDAKARK > 24 month1500.03.01502.0501.012.0NaN2.053.02.0NaNNaNNO31NaNNaN0
900003d165737109921ebd21f883cb8cff028b626TAMBACOUNDAK > 24 month4000.08.04000.01333.08.0NaN1620.09.0NaNNaNNaNNO45On-net 500F_FNF;3d8.00
user_idregionnetwork_durationtopup_amountnum_refill_amountmonthly_incomeincome_over_90days_3num_times_income_generatednum_of_connectionsinter_espresso_callorange_callstigo_callszone1_callszone2_callsvisiting_clientregularityactive_packfrequency_activating_top_packchurn
2154038ffffb2b8b63959b8a374e2a2ccaf2b9e521879adNaNK > 24 month1000.02.01000.0333.02.00.02.012.03.0NaNNaNNO12All-net 500F=2000F;5d2.00
2154039ffffc38e1c3cb77a88941e739c358fd96bce3238DAKARK > 24 monthNaNNaNNaNNaNNaNNaNNaN25.0NaNNaNNaNNO6NaNNaN0
2154040ffffccdae4d9097c20f95e87f5c89845cab4eff3SAINT-LOUISK > 24 month2000.04.01997.0666.05.00.057.01.0NaN2.0NaNNO21All-net 500F=2000F;5d2.00
2154041ffffd1d48dd02c059c82c70b8793c8dfa3d09593NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN0
2154042ffffd3057e31ff19496a3c00397a9a67d5037c52DAKARK > 24 month4800.04.04800.01600.014.07400.02.012.0NaNNaN0.0NO62Data:1000F=2GB,30d3.00
2154043ffffe85215ddc71a84f95af0afb0deeea90e6967NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO6NaNNaN0
2154044ffffeaaa9289cdba0ac000f0ab4b48f4aa74ed15THIESK > 24 month6100.015.05800.01933.015.0621.026.040.040.0NaNNaNNO55Data: 200 F=100MB,24H9.00
2154045fffff172fda1b4bb38a95385951908bb92379809NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN1
2154046fffff5911296937a37f09a37a549da2e0dad6dbbTHIESK > 24 month10000.011.07120.02373.013.0NaN0.0140.013.0NaNNaNNO28All-net 500F=2000F;5d12.00
2154047fffff6dbff1508ea2bfe814e5ab2729ce6b788c2NaNK > 24 monthNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNO62NaNNaN1